Learning Algorithm

Machine learning is a science of artificial intelligence that can be studied by computer algorithms that are automatically improved by experience. Machine learning is a multidisciplinary field that involves computers, informatics, mathematics, statistics, neuroscience, and more.

The simplest definition of machine learning comes from what Berkeley said: Machine learning is a branch of AI that explores ways to make computers more efficient based on experience.

We compare deep learning with machine learning and discuss their differences in all aspects. In addition to the comparison of deep learning and machine learning, we will also study their future trends.

For machine learning, the right data set and the right model structure are critical. Choosing the wrong data set or the wrong model structure may result in a poorly performing network model, and may even get a non-converged network model.

Developing new machine learning algorithms and describing how they work and why work is a science is often not necessary when developing a learning system.

We already know that we want to have a generalization ability of models learned through machine learning. In a straightforward way, it is that the learned model not only works well in the training samples, but also works in new samples well.